tidyGWAS

tidyGWAS standardizes and cleans genome-wide association study (GWAS) summary statistics to improve reproducibility and scalability for large-scale genetic analyses.

Key Features:

• Data Validation and Harmonization Separation: tidyGWAS cleanly separates data validation and harmonization from quality control steps.
• Variant Identifier Repair: Uses curated data to repair and harmonize variant identifiers across genome builds.
• Missing Column Imputation: Imputes missing columns in summary statistics when possible.
• Minimal Validation Filters: Validates summary statistics with minimal filters to preserve data.
• Partitioned Parquet Output: Saves outputs as partitioned parquet files optimized for high-throughput analysis via the arrow package.
• Performance Efficiency: Benchmarked as up to 6.5× faster and substantially more memory efficient than existing tools.
• Integrated Meta-Analysis: Implements fixed-effects meta-analysis directly on tidyGWAS output, achieving up to 10× speedup over existing software.

Scientific Applications:

• Polygenic Risk Prediction: Supports downstream analyses that depend on standardized GWAS summary statistics for genetic risk modeling.
• Drug Discovery: Enables applications in therapeutic development by facilitating clean genetic association data processing.
• Large-Scale Genetic Analyses: Improves scalability for high-throughput statistical genetic workflows.
• Meta-Analysis: Supports fixed-effects meta-analysis of multiple GWAS summary statistics datasets.

Methodology:

tidyGWAS uses curated reference data to repair and harmonize variant identifiers across genome builds, imputes missing columns when possible, validates summary statistics with minimal filters, and outputs partitioned parquet files optimized for the arrow package. A fixed-effects meta-analysis is implemented directly on tidyGWAS output.